Wait, maybe thermostat settings really do affect weight

I can’t resist posting this follow-up to last month’s discussion of a paper proposing that one of the reasons we’re getting fatter is that we heat our houses too much. Where’s the evidence, you demanded? Ask and you shall receive…

Peter Janiszewski over at Obesity Panacea recently had an interesting post describing a prospective study that followed 1,597 people for six years, looking for “relatively unexplored” factors that might predict who becomes obese and who doesn’t. One of the factors they looked at was the temperature people kept their homes at:

[A] twofold increased risk for both incident obesity and hyperglycemia was estimated in subjects living at an indoor temperature >20 C.

While there are all sorts of cause-and-effect questions to worry about, I should point out that the 315 people who were obese at the start of study weren’t included in the analysis of what caused obesity; similarly, the 618 people who started with hyperglycemia were excluded from the analysis of what caused hyperglycemia. So it wasn’t just that people who are already obese prefer warmer temperatures (which would be the opposite of what I’d naively expect anyway).

Of course, I should include the disclaimer from the paper:

It might be hypothesized that metabolic processes are favorably affected by an ambient temperature within the thermal neutral zone, that is, not requiring energy expenditure to be allocated to maintaining a constant body temperature. However, no evidence exists to support this and socioeconomic factors might confound these associations.

As Peter noted (rather forcefully!) in his blog post, this idea is way out there on the fringe. And no one, including me, is suggesting that it’s a dominant factor in causing obesity. But perhaps it’s actually worth considering as one of the elements in an “obesogenic environment.”

Couldn’t Diabetes be a confounder? The study finds that both pepole who are obese and and people who are hypergycemic at the end of the study have high room temperatures. A frequent symptom of diabetes is cold (esp. cold feet and other extremeties) as well as hyppergycemia. Obesitas is strongly related to diabetes.

Perhaps the study is fine, but I can’t resist taking a shot at it. This type of study makes me suspicious of data dredging.

It would also be intersting to know if the at least 303 people who were not obese, but hypergycemic at the beginning of the study were comparable in body weight to the ‘normal’ population but more likely to be obese at the end of the study. That would suggest an inversed relation between obesitas and hyperglycemia.

Hi RH: I’d certainly expect diabetes and obesity to be linked in many cases. But if they’ve done their statistics properly, those links should already be accounted for when they present their “odds ratio” results: the multiple regression model is calculating the independent contribution of each factor. Of course, that doesn’t mean it’s impossible for underlying factors to bias the results — there are any number of possible confounders here. Undiagnosed diabetes could be one; a preference for sedentary life that leads to a preference for higher temperatures could be another. Studies like this don’t “prove” anything, they just offer an opportunity to test hypotheses.

As for whether this is “data dredging”… If this was one of those enormous pre-existing data sets like the Framingham Heart Study, and then they went back and searched for factors that were linked to obesity, I’d call that data dredging. Here, they started with several hypothesis, collected data about the relevant factors, then waited six years to see whether those factors were linked to the outcomes in the predicted manner. It was a prospective study, so I don’t think it’s fair to call it data dredging.

Perhaps I should be a bit more modest if I can’t even spell ‘hyperglycemic’ and ‘hyperglycemia’ properly

As to the suggestion (and, indeed, it is no more than that, and of the unfounded sort, since I only read the abstract)of data dredging, what seemed a little odd to me was that the results section lists more factors than the objectives section. That suggest (and, again, no more than that) that a much larger number of hypotheses were tested.

Of course that may also be the result of having to boil down a large number of data to a short abstract.